In recent years, the use of cellular networks for wireless communications has grown tremendously. In a cellular network, multiple wireless users within a designated area, or cell, communicate with a single base-station. In a Time Division Multiple Access (TDMA) cellular network, each user communicates with the base-station in a time-multiplexed fashion. In other words, each user is allocated a slice of time (i.e., a TDMA time slot) during which it exchanges a burst (or packet) of data with the base-station. A burst is a sequence of digital symbols representing the data. The user must then wait until the other users have exchanged their bursts of data with the base-station before exchanging its next burst of data.
The quality of communication in a cellular network, often expressed as bit-error-rate (BER), can be degraded by a variety of factors. Three important factors that degrade the quality of communication and increase BER are multipath fading, noise (e.g., thermal noise), and interference.
There are essentially two types of multipath fading. Flat fading results when the primary ray of the transmitted signal arrives at the receiver at approximately the same time as one or more reflections of the transmitted signal. If the primary ray and the reflections have different amplitudes and phases, they combine at the reciever in a manner that produces variations in the received signal strength. These variations can include drops in signal strength over several orders of magnitude. When there are a large number of reflections, as is often the case in an urban cellular network with many sources for reflection (e.g., buildings), flat fading produces a Rayleigh distribution. Time dispersion is a second type of multipath fading that occurs when the reflections arrive at the receiver delayed in time relative to one another (i.e., their propagation paths have substantially different lengths). If the relative time delays are a significant portion of a symbol period, then intersymbol interference (ISI) is produced, wherein the received signal simultaneously contains information from several superimposed symbols. Thus, both types of multipath fading can corrupt the received signal at the receiver.
In addition to multipath fading, noise, such as thermal noise in the analog front end of a receiver, can also corrupt the received signal at the receiver. Noise typically has a white frequency distribution (e.g., constant energy at all frequencies) and a gaussian temporal distribution, leading to the term additive, white, guassian noise (AWGN).
The third factor that can corrupt the received signal at the receiver is co-channel interference (CCI). CCI is the result of receiving the desired signal along with other signals which were transmitted from other radios but occupy the same frequency band as the desired signal. There are many possible sources of CCI. For example, an indirect source of CCI is adjacent channel interference (ACI). ACI is the result of side-band signal energy from radios operating at neighboring frequency bands that leaks into the desired signal's frequency band. A more direct source of CCI is signal energy from other radios operating at the same frequency band as the desired signal. For example, a cellular radio in a distant cell operating at the same frequency can contribute CCI to the received signal in the cell of interest.
All of these sources of signal corruption, but especially CCI and ISI, can significantly degrade the performance of a wireless receiver in a TDMA cellular network. Furthermore, tolerance to CCI determines the frequency reuse factor and therefore the spectral efficiency (Erlang/Hertz/Basestation) of the cellular network. Since received signals in a wireless system such as a TDMA cellular network typically comprise desired symbols as well as CCI, ISI, and noise, successful design of a wireless system requires solutions that address all these problems.
The problem of flat or Rayleigh fading can be addressd by implementing a receiver with two or more physically separated antennas and employing some form of spatial diversity combining. Spatial diversity takes advantage of the fact that the fading on the different antennas is not the same. Spatial diversity can also address interference by coherently combining the desired signal (i.e., desired symbols) from each antenna while cancelling the interfering signal (i.e., interfering symbols) from each antenna.
CCI differs from ISI in several aspects. First, the energy of CCI can be significantly lower than the energy of ISI due to larger exponential decay of the (usually) longer CCI propagation paths. This imbalance of energy causes algorithms designed to simultaneously reduce both CCI and ISI to combat ISI more than CCI. Second, in order to remove ISI, the desired user's channel impulse response must be estimated. This channel impulse response characterizes the ISI of the desired user's propagation channel. However, CCI contributes interfering, undesired symbols into the received signal. Because these interfering symbols can mask the structure of the ISI, channel estimation in the presence of CCI could be inaccurate. Third, CCI and ISI have different characteristics in the spatial and temporal domains (i.e., angles of arrival and channel impulse responses). These three differences between CCI and ISI can be utilized to separate and remove them from the desired symbols in the received signal.
The optimal theoretical solution to the CCI and ISI problems is a receiver that employs diversity combining and a multi-channel maximum-likelihood-sequence-estimator (MLSE) equalizer wherein the individual channel vectors (i.e., the discrete-time channel impulse responses) are known for all signals (i.e the desired signal and all its reflections and all the interferers). The MLSE receiver jointly demodulates both the desired and undesired signals. However, in a practical cellular network, the channels for the CCI are either unknown or can be only approximately determined. Furthermore, in certain cases CCI could have different modulation schemes and baud rates, and hence a multi-channel MLSE becomes much more complicated. Therefore, various suboptimal schemes which treat CCI as noise and focus on eliminating ISI with an equalizer have been proposed. They can be broadly classified as follows.
One class of receivers use minimum mean-square error (MMSE) criteria to provide an equalizer that reduces CCI and ISI simultaneously, such as space-only and space-time MMSE receivers. These receivers are well-known in the art and are fairly robust to CCI. However, this class of receivers implement symbol-by-symbol decision, and therefore, they are not optimal for ISI which spreads the information content of the received signal accross several symbols. Besides, they suffer from noise enhancement inherent in the MMSE approach due to channel inversion. The second class of receivers that treat CCI as noise use time-only or space-time minimum mean-square error decision feedback equalizers (MMSE/DFE). This class of receivers can perform adequately at a high signal-to-interference-plus-noise ratio (SINR). However, catastrophic error propagation can appear when the CCI is strong or when the received signals are in a deep fade. A third class of receivers that treat CCI as noise implement MLSE-based algorithms which include spatial-whitening/Viterbi and spatial-temporal-whitening/matched filter/Viterbi equalizers. The main advantage of this class of receivers is that they effectively combat ISI without producing noise enhancement or error propagation. However, the covariance matrix of the CCI must be known. All three classes of receivers described above require either accurate estimation of channel information or the covariance matrix of CCI plus noise. However, in practical situations, the presence of severe CCI impairs the accuracy of estimation of these parameters, and hence the receiver performance. It is therefore desirable to provide a digital receiver for a TDMA network which provides improved estimation of the desired symbols in a received signal that includes the desired symbols, CCI, ISI, and noise.